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SEA-HELM: Southeast Asian Holistic Evaluation of Language Models

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arxiv 2502.14301 v2 pith:QHOILLGG submitted 2025-02-20 cs.CL cs.AI

classification cs.CLcs.AI
keywords sea-helmevaluationlanguagesmodelsasianbenchmarkscapabilitiesholistic
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA) region, a comprehensive and culturally representative evaluation suite for the SEA languages has not been developed thus far. Here, we present SEA-HELM, a holistic linguistic and cultural LLM evaluation suite that emphasises SEA languages, comprising five core pillars: (1) NLP Classics, (2) LLM-specifics, (3) SEA Linguistics, (4) SEA Culture, (5) Safety. SEA-HELM currently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner. We make the SEA-HELM evaluation code publicly available.

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  1. Mangosteen: An Open Thai Corpus for Language Model Pretraining

    cs.CL 2025-07 conditional novelty 6.0 of 10

    An open 47B-token Thai pre-training corpus and a Thai-adapted data cleaning pipeline, with ablations showing quality gains and an 8B model that improves on Thai benchmarks.

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